Calibration Score#

This notebook provides an overview for using and understanding the Calibration Score check.

Structure:

What is the Calibration Score check?#

The CalibrationScore check calculates the calibration curve with brier score for each class. Calibration refers to the relationship between the model probabilities for one label to the ground truth (the label). For instance, a probability of 0.7 for class A represents that there is 70% chance the true label of this sample is actually class A.

Calibration curves (also known as reliability diagrams) compare how well the probabilistic predictions of the classifier are calibrated by plotting the true frequency of one label against its predicted probability.

The Brier score metric may be used to assess how well a classifier is calibrated (Brier score).

Imports#

import warnings

import pandas as pd
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import CalibrationScore
from deepchecks.tabular.datasets.classification import adult


def custom_formatwarning(msg, *args, **kwargs):
    # ignore everything except the message
    return str(msg) + '\n'

warnings.formatwarning = custom_formatwarning

Binary Classification#

# Generate data & model
# -----------------------
# The dataset is the adult dataset which can be downloaded from the UCI machine
# learning repository.
#
# Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml].
# Irvine, CA: University of California, School of Information and Computer Science.
train_ds, test_ds = adult.load_data()
model = adult.load_fitted_model()

Run the check#

Calibration Metric


Multi-class classification#

# Generate data & model
# -----------------------
iris = load_iris(as_frame=True)
clf = LogisticRegression()
frame = iris.frame
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=55)
clf.fit(X_train, y_train)
ds = Dataset(pd.concat([X_test, y_test], axis=1),
            features=iris.feature_names,
            label='target')

Run the check#

check = CalibrationScore()
check.run(ds, clf)
Calibration Metric


Total running time of the script: ( 0 minutes 5.729 seconds)

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